This disclosure is in the field of mass spectrometry. This disclosure relates generally to computer readable storage mediums, methods and systems for normalizing and standardizing mass spectrometry data. Normalization and standardization are necessary to compare data across instruments or time. Stable isotope dilution methods or external standard calibrations are not scalable to hundreds to thousands of chemicals in complex biological extracts. Surrogate standardization is limited by chemical properties and dynamic range.
Currently, no universal normalization protocol has been accepted for use with liquid chromatography-mass spectrometry (LC-MS) based metabolomics. A proposed method for normalization, called NOMIS (normalization using optimal selection of multiple internal standards), was published in 2007 by Sysi-Aho et al. (Sysi-Aho et al., Normalization method for metabolomics data using optimal selection of multiple internal standards, BMC Bioinformatics, 2007, 8:93). NOMIS provides a sound technique for normalization of LC/MS data by using a mathematical model that optimally assigns normalization factors for each metabolite measured based on internal standard profiles. However, these analyses use log transformation that are unable to deal with features that contain a zero value, use no external standard, and have only been shown to be suitable for lipid profiling with a mass to charge range (m/z) of 300-1600.
Another strategy developed for normalization of mass-spectrometry data utilizes a calibration transfer algorithm where the signal variation observed for a calibration model is used to correct experimental intensities to a date the calibration model was constructed. Pavon et al. describes this technique and utilizes the average intensities of a number of calibration transfer samples for the correction (Pavón et al., Calibration Transfer for Solving the Signal Instability in Quantitative Headspace-Mass spectrometry, Anal. Chem. 2003, 75, 6361-6367 and Pavón et al., Strategies for qualitative and quantitative analyses with mass spectrometry-based electronic noses, Trends Anal. Chem., 2006, 25, 257-266).
Deport et al. describes a method for normalization of gas chromatography-mass spectrometry (GC-MS) data where a number of internal standards are analyzed with a sample and each sample peak area is normalized against each of the possible internal standard combinations to determine which combination provides the best discrimination of a specific sample peak (Deport et al., Comprehensive combinatory standard correction: A calibration method for handling instrumental drifts of gas chromatography-mass spectrometry systems, J. Chrom A, 2006, 1116, 248-258).